AI-Assisted Cross-Study Synthesis in Genome Editing: Comparing Long-Context Strategies and Uncovering Latent Contradictions in the CRISPR-Cas9 Guide RNA Prediction Literature

Abstract

Predicting CRISPR-Cas9 guide RNA efficiency and off-target activity is a precondition for precise genome editing. Computational models have progressively incorporated chromatin accessibility and epigenetic descriptors into their feature sets, yet synthesising findings from independently published studies—especially when those studies contradict one another—remains an unresolved methodological gap. Large Language Models (LLMs) have been proposed as a route to automate cross-study synthesis, but their utility depends on a constraint that receives less attention than model architecture: how much of the source text actually reaches the model at inference time. Cloud-based models process 48,000-token corpora without hardware limitations, but at the cost of data leaving the local environment and with limited reproducibility across API versions. Local RAG systems avoid the cloud dependency while fragmenting the input, discarding the global context needed to link biological arguments that are distributed across separate papers. We benchmark these strategies using a corpus of four CRISPR-Cas9 efficiency prediction studies and apply the Reduced Interaction Sampling (RIS) engine—a local sparse attention method—to retain the full sequence within the memory envelope of a laboratory server. Preserving that context uncovers three latent inconsistencies. The static epigenetic markers used in DeepCRISPR (CTCF, DNase I) show near-zero Spearman correlations with off-target cleavage (ρ≤0.07), while nucleosome positioning scores from the Block Decomposition Method reach ρ=0.388–0.423. The sequence-only Apindel model was published in June 2022 without incorporating nucleosome descriptors reported in the concurrent literature. The benchmark review by Konstantakos et al. attributed 10–20% of rank correlation to epigenetics—a figure that reflects the weak feature subset evaluated, not a ceiling on chromatin influence. These discrepancies are invisible when papers are read individually or retrieved as chunks; they become traceable only when the full corpus is processed as a single context window. An independent empirical analysis of 2000 CRISPR-Cas9 off-target cleavage events provides evidence consistent with this pattern: static epigenetic markers yield |ρ|≤0.11, whereas computed NuPoP Affinity descriptors reach r=−0.622 (p<10−210). On a 30-question cross-study synthesis benchmark (5 independent seeds), baseline accuracy is 53.33%, RAG 60.00%, and RIS (30 seeds, 3% density) 70.00% (p<0.0001, t-test vs. RAG, σ=0.00% for all configurations).